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European Journal of Heart Failure 2005 7(5):803-808; doi:10.1016/j.ejheart.2005.03.001
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© 2005 European Society of Cardiology

Prevalence and predictors of anxiety and depression in a sample of chronic heart failure patients with left ventricular systolic dysfunction

J.E. Hawortha,*, E. Moniz-Cooka, A.L. Clarkb, M. Wanga, R. Waddingtonc and J.G.F. Clelandb

a Department of Clinical Psychology, Post Graduate Medical Institute, Hertford Building University of Hull, Kingston-upon-Hull HU6 7RX, United Kingdom
b Department of Academic Cardiology, University of Hull Castle Hill Hospital, Cottingham HU16 5JQ, United Kingdom
c Information by Design University of Hull, United Kingdom

* Corresponding author. Present address: Department of Clinical and Health Psychology, St. James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Tel.: +44 113 2065897; fax: +44 113 2064079. E-mail address: Jane.Haworth{at}leedsth.nhs.uk


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 
Objective: To determine the prevalence and predictors of anxiety and depression in patients with heart failure due to Left Ventricular Systolic Dysfunction (LVSD).

Background: Psychological adjustment to Chronic Heart Failure (CHF) can be poor, with the prevalence of depression in out-patients ranging from 13% to 48%. The prevalence of anxiety disorders in this population is unknown and the factors that predict anxiety and depression are not well understood.

Methods: 100 out-patients from a community heart failure programme completed a clinical diagnostic interview—the Structured Clinical Interview (SCID-I), to evaluate anxiety and depression. Mean age was 67 ± 11 years, 17% were women and 91% were NYHA Class II or III. Other standardised measures were of cognition, biomedical status, social support and previous physical and mental health history.

Results: The prevalence rates of anxiety and depression (all subtypes) were 18.4% and 28.6%, respectively. Predictors of depression included a reported history of mental ill-health and NYHA class. Predictors of anxiety included a reported history of mental ill-health, co-morbid physical illness (diabetes and angina) and NYHA class. Severity of LVSD did not predict either anxiety or depression.

Conclusions: Both anxiety and depression are common in CHF patients. The data on the predictors of poor psychological adjustment might assist in targeting bio-psychosocial intervention for patients who are at most at risk of anxiety and depression, within community CHF disease management programmes.

Key Words: Heart failure • Out-patients • Depression • Anxiety

Received December 22, 2004; Revised February 22, 2005; Accepted March 24, 2005


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 
Chronic Heart Failure (CHF) is a common [1], complex syndrome associated with poor prognosis [2] and has an adverse impact on quality of life [3].

Research into psychological adjustment to CHF has generally focussed on depression, with prevalence rates estimated at 17–85% [4,5]. Most studies used self-report questionnaires to estimate prevalence, when these may not reflect a clinical diagnosis of depression [5–12]. Furthermore, many of these questionnaires (such as CES-D [13] and the BDI [14]) may, for patients with multiple health conditions, be influenced by somatic symptoms that are unrelated to mood. Therefore, reported prevalence rates of depression in CHF patients may be overestimated. Where clinical interview schedules have been used to diagnose depression [4,15–17], the sample population has been limited to medical in-patients, and there remains the potential for inaccurate estimates of depression in the total CHF population. The prevalence of anxiety in CHF patients has been under-researched [18], with suggestions that prevalence rates may be no different from those of healthy volunteers [10]. Thus, the prevalence of both anxiety and depression in an out-patient population with CHF remains unknown.

Accurate diagnosis is important since depression in CHF has been associated with an adverse prognosis [16,19], reduced functional ability [5] and greater use of health care resources [16,20]. An understanding of the predictors of mood disorders may contribute to the management of patients with CHF. Depression has been associated with the severity of CHF in some studies [8] but this is not always the case [5,9]. It has also been associated with poor social support [21] but the relative contribution of bio-medical and psychosocial factors to the development of mood disorders in CHF needs clarification.

This study examines the prevalence of depression and anxiety in a sample of CHF out-patients using a diagnostic clinical reference standard (i.e., Structured Clinical Interview for DSM-IV (SCID-I)) [22] and both bio-medical and psychosocial predictors.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 5. Conclusions
 References
 
2.1. Ethical approval
The Local Research Ethics Committee approved this study and participants gave written consent to a clinical interview and use of their medical records.

2.2. Participants
221 non-institutionalized patients (>18 years of age) attending a community heart failure chronic disease management programme were initially contacted by post to participate in this study. Patients were included if they had a clinical diagnosis of CHF (<3 months) based on clinical signs and symptoms, left ventricular systolic dysfunction (ejection fraction < 40% generally determined by echocardiography) and were receiving chronic diuretic therapy. Patients with Implantable Cardioverter Defibrillators (ICDs) were excluded since these patients were considered a separate sub-group and were involved in a separate study [23]. One hundred of these consented to a face-to-face interview. For a sample of 100 patients, if assuming there is moderate relationship between CHF and depression of 0.3, the expected power is calculated at 86% [24]. Two further patients were excluded from data analysis because of probable cognitive impairment (i.e., score <24 points on the Mini-Mental State Examination, MMSE) [25].

2.3. Cognitive and psychosocial assessments
Diagnoses of depression and anxiety were made during face-to-face interviews using the Structured Clinical Interview for Diagnostic and Statistical Manual of mental disorders (SCID-I) [22], a criterion reference method for diagnosing mood disorders according to DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edn.) [26]. This covers the emotional, physiological (e.g., changes in sleep and appetite) and cognitive aspects of anxiety and depression. Diagnostic criteria require symptoms to be present for 2 weeks in the last month. Depression diagnoses are mutually exclusive (i.e., only one category can be used) but anxiety disorders are not. The term ‘mood disorders— is used in this paper to include anxiety (all sub-types), depression (all subtypes) and mixed anxiety and depression (see Table 1). The interview lasted 90 min on average, but was shorter for participants who were not depressed or anxious. All participants completed (and reported enjoying) the interview.


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  		Table 1 Description of mood disorders

 
Social support was assessed using the self-report Medical Outcomes Study Social Support Survey (MOS Social Support) [27]. This measures the number of supportive people (structural support) and the type of support available. Nineteen items of social support, categorized into four types, are rated on a 1–5 Likert scale ranging from ‘support available none of the time— to ‘support available all of the time.— For this study, summated total scores were used, with higher scores indicating greater perceived availability. Internal consistency for the MOS Social Support is good (alpha=0.97) and its validity is impressive [28].

The MMSE [25] was used to screen for cognitive impairment. This is a 19-item test of orientation, recall, short-term memory, calculation and language. Scores of <24 points (out of a total of 30) suggests "probable cognitive impairment" [29], which was our exclusion criterion.

Left Ventricular Ejection Fraction (LVEF) was assessed by echocardiography. Aetiology, NYHA class and medication were recorded.

Demographics included living and employment status. Self-report data of medical history (history of cardiac and cardiovascular events and interventions, history of other major illnesses or major surgery) and co-morbid medical conditions (diabetes, hypertension, respiratory disease, arthritis and any other conditions) were collected. Data on mental health history was collected using the same procedure from another study with CHF patients [15].

2.4. Statistical analysis
Predictors of SCID-I diagnoses for anxiety and depression were explored using logistic regression, since data was essentially binary in nature. Given the range of potential predictor variables, two steps were taken to avoid spurious variables being included, since this could have rendered a perfect but meaningless solution [30]. Firstly, the association between each predictor variable and SCID-I diagnosis was tested with either t-test or chi-squared test and only significantly associated (p<0.05) variables were included in the regression. Secondly, variables were grouped into theoretically linked blocks (see Table 2 for variables in each block) and each block was entered into an initial step-wise logistic regression analysis, using an exit level of p<0.10. The model therefore included only those variables that were significant in the previous step and explained the highest variance and percentage accuracy in classification, within the regression. Analyses were conducted separately for anxiety and depression. A second model that excluded anti-depressants as a predictor for depression was derived, since ‘anti-depressants— as a predictor variable may have had a swamping effect on the data, thus rendering it meaningless.


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  		Table 2 Theoretically linked blocks used in the regression analysis

 

   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 5. Conclusions
 References
 
3.1. Participants
Of the total sample of 221 CHF patients (165 male and 56 female), 121 (55%) responded to the initial postal survey. Of these, the first 100 consenting participants (83 male and 17 female) were interviewed.

The mean age of participants was 67 years (standard deviation: S.D.=11, Range=35–92). Eighty-eight percent were retired and 20% lived alone. Their mean LVEF was 36% (S.D.=9). The majority (87%) reported co-morbid physical problems. Most participants had not been hospitalized (66%) in the 7 months prior to initial contact and the median number of in-patient days was 0 (Inter-Quartile Range=0–2). At interview, 12% had been prescribed an anti-depressant and 2% had been prescribed an anxiolytic. Table 3 summarises participant characteristics. The mean MMSE score was 29 (S.D.=2). The mean MOS Social Support score was moderately high at 76 (S.D.=21).


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  		Table 3 Medical characteristics of the sample (n=98)

 
3.2. Prevalence of mood disorders
Table 4 shows that over one quarter (29%) of participants had a depressive disorder and almost a fifth (18%) had at least one anxiety disorder. Major depression was the commonest depression subtype, occurring in 14% of participants. Two percent of participants had a diagnosis of both PD and GAD, 6% had a depressive disorder and PD and 9% had a depressive disorder and GAD. One participant had a mixed anxiety and depressive disorder. There were no diagnoses of manic episodes (i.e., bipolar mood disorders), obsessive–compulsive disorder or post-traumatic stress disorder in this CHF population.


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  		Table 4 Prevalence of mood disorders (DSM-IV definitions) diagnosed by the SCID-I in the interviewed participants (n=98)

 
3.3. Predictors of anxiety and depression diagnoses
Bivariate analysis showed several factors to be associated with a diagnosis of depression and anxiety (see Table 5). Those with depression had significantly lower social support scores. The diagnosis of a mood disorder (i.e., anxiety and depression) was associated with significantly worse functional status (NYHA Class) and a history of mental ill-health. A diagnosis of an anxiety disorder was associated with several co-morbid physical conditions.


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  		Table 5 Differences per block (see Table 2 for contents of each block) for the anxiety and depression diagnoses for each significant variable (n=98)

 
The multivariate analysis for prediction of a diagnosis of depression consisted of the following variables: ‘sum of social support— (OR, 0.96; 95% C.I. 0.9–1.0); ‘NYHA Class— (OR, 1.77; 95% C.I. 0.9–3.4); and ‘anti-depressants— (OR, 113.86; 95% C.I. 11.2–1159.4). This model explained 35% of the variance and had 85% accuracy in the classification of depression (see Table 6a).


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  		Table 6a The best models for predicting SCID-I diagnosis of depression

 
The second model that excluded anti-depressants as a predictor consisted of the following variables (see Table 6a): ‘NYHA Class— (OR, 2.16; 95% C.I. 1.1–4.3) and ‘any reported history of mental ill-health— (OR, 4.85; 95% C.I. 1.8–13.2). This model explained 16% of the variance and had 76% accuracy in classification.

Given that ‘social support— dropped out of the second model when anti-depressants were excluded, the relationship between anti-depressants, social support and diagnosis of depressive illness was investigated further. Those participants who were depressed but not receiving anti-depressant treatment had a mean social support score of 56 (S.D.=24) and those participants classified as depressed who were also receiving treatment with anti-depressants had a mean score of 86 (S.D.=15). This difference between the two groups was statistically significant (p=0.001).

The multivariate analysis for the prediction of a diagnosis of anxiety consisted of the following variables: ‘any reported history of mental ill-health— (OR, 11.7; 95% C.I. 3.1–44.0); ‘diabetes— (OR, 2.1; 95% C.I. 1.0–4.3); ‘angina— (OR, 3.9; 95% C.I. 1.3–18.8); and NYHA class— (OR, 1.9; 95% C.I. 1.1–3.8). This model explained 29% of the variance and had 86% accuracy in the classification of anxiety (see Table 6b).


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  		Table 6b The best model for predicting SCID-I diagnosis of anxiety

 

   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
5. Conclusions
 References
 
This is the first substantial study to assess mood disorders in the out-patient setting, using a standard structured clinical reference point interview. Both anxiety and depression were common (29% for depression, 11% for GAD and 8% for PD) and higher than reported for healthy community dwelling older adults (14% for depression, 4% for GAD and <1% for PD) [31,32]. The prevalence of anxiety and depression in CHF out-patients is similar to that of patients recovering from a myocardial infarction [33]; and for depression, similar to those of hospitalised heart failure patients [4,15,16]. Our rates of depression in CHF out-patients are lower than those of a recent self-report questionnaire study [12] using the BDI [14] to measure depression but since the BDI relies on self-report of somatic symptoms, it risks overestimating depression in older patients with multiple co-morbid diseases. Only one previous small self-report study comprising 50 hospitalised participants investigated the prevalence of anxiety in heart failure patients, exclusively with idiopathic dilated cardiomyopathy [17]. These authors noted a slightly higher prevalence rate of anxiety (i.e., GAD 16% and PD 12%) than in our study.

4.1. Predictors of depression and anxiety
Two models were created for the prediction of depression. The first found that social support had no independent association with depression. This was surprising because the association between low perceived social support and depression has strong empirical support in the mental health literature [34]. Social support may act as a buffer to depression during distressing life events. The lack of association found in this model may have been due to inclusion of the anti-depressant variable, which had a strong interaction with social support and depression. Concepts of ‘help-seeking— behaviour may explain this finding: i.e., patients who see themselves as ‘depressed— because they are prescribed an anti-depressant may be more likely to seek a confidant, or those who have social support may be more likely to be encouraged by others to seek help from their physician for depression, and thus receive an anti-depressant prescription. The influence of social support remains important since Krumholz et al. [7] found that patients with no support were 2.7 times more likely (OR, 2.69; 95% C.I. 1.22–5.94) to experience a cardiovascular event 12 months after hospitalisation for heart failure. Psychological therapies for depression in CHF have not yet been evaluated [35]. The present data on the predictors of poor psychological adjustment to CHF suggests that emotional and social support-enhancing psychosocial interventions to reduce the risk of depression in CHF disease management programmes should target those who do not have strong social networks.

When anti-depressants were removed from the model, only two variables (NYHA Class and a reported history of mental ill-health) contributed to the diagnosis of depressive illness. The relationship between prior mental ill-health and depression in patients with CHF is consistent with previous data [15] and studies in patients after myocardial infarction [36]. Greater functional impairment (as indicated by NYHA class) predicted both anxiety and depression. One potential explanation for this could be that increased functional disability results in reduced activity and social contacts, with more time to dwell on health conditions and increased feelings of helplessness and loneliness. Loneliness and feelings of helplessness are common predictors of poor psychological adjustment to chronic disease.

The association between anxiety and the presence of co-morbid physical conditions may reflect the complexity of coping with multiple illnesses. Integrative disease management protocols that include psycho-educative procedures such as written information on how individuals may manage their particular co-morbid diseases could minimise patient anxiety within comprehensive multi-component CHF disease management programmes.

4.2. Clinical and research implications
Considering the high prevalence of anxiety and depression in patients with CHF and their association with increased mortality and readmission [16,19], it might be expected that considerable effort would be directed at safe and effective management of mood disorders. Unfortunately, this is not the case; longitudinal intervention research needs to clarify the relative merits of social support, psychological therapy and anti-depressant medication for mood disorders in the CHF population.

4.3. Limitations
Patients were predominantly male and younger than the epidemiological age of heart failure, but this was largely accounted for by the restriction to patients with impaired left ventricular systolic dysfunction [37]. All available patients were invited to participate; only 56% responded. Responders had less cognitive impairment than expected [8] and were younger than non-responders. This may have affected prevalence rates, as a recent study [12] suggested a tendency towards increased depression rates in younger patients with CHF. It is also possible that those with mood disorders were more likely to participate in the hope of receiving help. However, non-responders may have had a higher prevalence of mood disorders and failed to respond due to the effects of low mood.

We did not formally measure patients' awareness of their CHF diagnosis and time since diagnosis. A significant minority of participants appeared unaware of their diagnosis. Since awareness and duration of illness can influence psychological adjustment in chronic disease, this may have either contributed to, or protected from, mood disorder [18]. Finally, this cross-sectional study provides no information on the incidence and recovery from mood disorders. Longitudinal studies are now underway.


   5. Conclusions
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusions
References
 
Diagnosable depression and anxiety disorders are common in patients with CHF living at home. Patients with more severe functional disability, a reported history of mental ill-health and those with co-morbid conditions appeared to be at greater risk of a mood disorder. Longitudinal studies of the natural history of mood disorders and randomised controlled trials investigating the relative merits of good control of heart failure symptoms, social support and psychological interventions for their improvement are urgently required.


   Acknowledgement
 
The authors thank Dr Donald Sharp, Institute of Rehabilitation, University of Hull, for his advice on administering the SCID-I.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 

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